some minor changes + R package dependency

DESCRIPTION

@@ -16,10 +16,12 @@ Imports:
  stats,
  survival,
  DescTools,
- MASS
+ MASS,
+ boot
 Suggests:
  knitr,
- testthat (>= 2.0)
+ testthat (>= 2.0),
+ ggplot2
 VignetteBuilder:
  knitr
 biocViews:

NAMESPACE

@@ -15,15 +15,8 @@ export(process_agd)
 export(resid_plot)
 export(survfit_makeup)
 import(DescTools)
+import(MASS)
+import(boot)
+import(graphics)
 import(stats)
-importFrom(graphics,abline)
-importFrom(graphics,axis)
-importFrom(graphics,hist)
-importFrom(graphics,legend)
-importFrom(graphics,lines)
-importFrom(graphics,par)
-importFrom(graphics,points)
-importFrom(survival,Surv)
-importFrom(survival,cox.zph)
-importFrom(survival,coxph)
-importFrom(survival,survfit)
+import(survival)

R/bucher.R

@@ -24,9 +24,7 @@
 #' \item{pval}{p-value of Z-test, with null hypothesis that \code{est} is zero}
 #' }
 #' @export
-#' @import stats
-#'
-#' @examples
+
 bucher <- function(trt, com, conf_lv = 0.95) {
  est <- trt$est - com$est
  se <- sqrt(trt$se^2 + com$se^2)

R/maic_unanchored_tte.R

@@ -16,12 +16,9 @@
 #' \item time - numeric column, observation time of the \code{status}; unit in days
 #' }
 #'
-#' @return
-#' @importFrom survival Surv survfit coxph cox.zph
-#' @importFrom graphics par axis lines points legend abline
+#' @return A list of KM plot, analysis table, and diagnostic plot
 #' @export
-#'
-#' @examples
+
 maic_tte_unanchor <- function(useWt, dat, dat_ext, trt, trt_ext,
  time_scale = "month", endpoint_name = "OS",
  transform = "log") {

R/maicplus.R

@@ -0,0 +1,30 @@
+#' maicplus: open source R package for MAIC
+#'
+#' A package for running matching adjusted indirect comparison
+#'
+#' This package facilitates performing matching adjusted indirect comparison
+#' (MAIC) analysis where the endpoint of interest is either time-to-event
+#' (e.g. overall survival) or binary (e.g. objective tumor response).
+#'
+#' @docType package
+#' @name maicplus-package
+#'
+#'
+
+#' @import graphics
+NULL
+
+#' @import stats
+NULL
+
+#' @import survival
+NULL
+
+#' @import DescTools
+NULL
+
+#' @import MASS
+NULL
+
+#' @import boot
+NULL

R/matching.R

@@ -1,12 +1,10 @@
 # Functions for matching step: estimation of individual weights
 
-# functions to be exported ---------------------------------------
-
 #' Derive individual weights in the matching step of MAIC
 #'
-#' Assuming data is properly processed, this function takes individual patient data (IPD) with centered covariates
-#' (effect modifiers and/or prognostic variables) as input, and generates weights for each individual in IPD trial that
-#' matches the chosen statistics of those covariates in Aggregated Data (AgD) trial.
+#' This function takes individual patient data (IPD) with centered covariates
+#' (effect modifiers and/or prognostic variables) as input and generates
+#' weights for each individual in IPD trial to match the covariates in aggregate data.
 #'
 #' @param data a numeric matrix, centered covariates of IPD, no missing value in any cell is allowed
 #' @param centered_colnames a character or numeric vector (column indicators) of centered covariates
@@ -27,8 +25,7 @@
 #' \item{opt}{R object returned by \code{base::optim()}, for assess convergence and other details}
 #' }
 #' @export
-#'
-#' @examples
+
 estimate_weights <- function(data, centered_colnames = NULL, start_val = 0, method = "BFGS", ...) {
  # pre check
  ch1 <- is.data.frame(data)
@@ -116,7 +113,6 @@ estimate_weights <- function(data, centered_colnames = NULL, start_val = 0, meth
 #' @param main_title a character string, main title of the plot
 #'
 #' @return a plot of unscaled or scaled weights
-#' @importFrom graphics hist
 #' @export
 
 plot_weights <- function(wt, bin_col = "#6ECEB2", vline_col = "#688CE8", main_title = "Unscaled Individual Weights") {
@@ -155,13 +151,13 @@ plot_weights <- function(wt, bin_col = "#6ECEB2", vline_col = "#688CE8", main_ti
 #' This function checks to see if the optimization is done properly by checking the covariate averages
 #' before and after adjustment.
 #'
-#' @param optimized object returned after calculating weights using \code{\link{estimate_weights}}
+#' @param match_res object returned after calculating weights using \code{\link{estimate_weights}}
 #' @param processed_agd a data frame, object returned after using \code{\link{process_agd}} or
 #' aggregated data following the same naming convention
 #'
 #' @import DescTools
 #'
-#' @return data.frame of weighted and unweighted covariate averages of the IPD
+#' @return data.frame of weighted and unweighted covariate averages of the IPD, and average of aggregate data
 #' @export
 #'
 #' @examples
@@ -214,7 +210,7 @@ plot_weights <- function(wt, bin_col = "#6ECEB2", vline_col = "#688CE8", main_ti
 #' )
 #'
 #' print(check)
-check_weights <- function(optimized, processed_agd) {
+check_weights <- function(match_res, processed_agd) {
  ipd_with_weights <- optimized$data
  match_cov <- optimized$centered_colnames
 

R/process_data.R

@@ -18,7 +18,6 @@
 #' by legal suffixes (i.e. MEAN, MEDIAN, SD, COUNT, or PROP).
 #'
 #' @examples
-#' # example
 #' target_pop <- read.csv(system.file("extdata", "aggregate_data_example_1.csv",
 #' package = "maicplus", mustWork = TRUE
 #' ))
@@ -33,20 +32,6 @@
 #' target_pop2 <- process_agd(target_pop2)
 #' target_pop3 <- process_agd(target_pop3)
 #'
-#' # another example
-#' target_pop <- data.frame(
-#' STUDY = "Study_XXXX",
-#' ARM = "Total",
-#' N = 300,
-#' AGE_MEAN = 51,
-#' AGE_MEDIAN = 49,
-#' AGE_SD = 3.25,
-#' SEX_MALE_COUNT = 147,
-#' ECOG0_COUNT = 105,
-#' SMOKE_PROP = 58 / 290
-#' )
-#' process_agd(target_pop)
-#'
 #' @return pre-processed aggregate level data
 #' @export
 
@@ -155,7 +140,7 @@ dummize_ipd <- function(raw_ipd, dummize_cols, dummize_ref_level) {
 #'
 #' @param ipd IPD variable names should match the aggregate data names without the suffix.
 #' This would involve either changing the aggregate data name or the ipd name.
-#' For instance, if we binarize SEX variable with MALE as a reference, function names the new variable as SEX_MALE.
+#' For instance, if we binarize SEX variable with MALE as a reference using [dummize_ipd], function names the new variable as SEX_MALE.
 #' In this case, SEX_MALE should also be available in the aggregate data.
 #' @param agd pre-processed aggregate data which contain STUDY, ARM, and N. Variable names should be followed
 #' by legal suffixes (i.e. MEAN, MEDIAN, SD, or PROP). Note that COUNT suffix is no longer accepted.
@@ -247,13 +232,14 @@ complete_agd <- function(use_agd) {
 }
 
 
-#' helper function: transform TTE ADaM data to suitable input for survival R pkg
+#' helper function: transform TTE ADaM data to suitable input for survival R package
 #'
 #' @param dd data frame, ADTTE read via haven::read_sas
 #' @param time_scale a character string, 'year', 'month', 'week' or 'day', time unit of median survival time
 #' @param trt values to include in treatment column
 #'
 #' @return a data frame that can be used as input to survival::Surv
+
 ext_tte_transfer <- function(dd, time_scale = "month", trt = NULL) {
  time_units <- list("year" = 365.24, "month" = 30.4367, "week" = 7, "day" = 1)
 

R/survival-helper.R

@@ -30,13 +30,12 @@ medSurv_makeup <- function(km_fit, legend = "before matching", time_scale) {
 
 
 
-#' helper function: makeup `survival::survfit` object for km plot
+#' Helper function to select a set of variables used for Kaplan-Meier plot
 #'
 #' @param km_fit returned object from \code{survival::survfit}
-#'
+#' @return a list of data frames of variables from \code{survival::survfit}. Data frames are divided by treatment.
 #' @export
-#'
-#' @return a list of data frames, one element per treatment
+
 survfit_makeup <- function(km_fit) {
  kmdat <- data.frame(
  time = km_fit$time,
@@ -64,7 +63,7 @@ survfit_makeup <- function(km_fit) {
 #' @param endpoint_name a character string, name of the endpoint
 #'
 #' @return a KM plot
-km_makeup <- function(km_fit_before, km_fit_after = NULL, time_scale, trt, trt_ext, endpoint_name = "") {
+km_plot <- function(km_fit_before, km_fit_after = NULL, time_scale, trt, trt_ext, endpoint_name = "") {
  timeUnit <- list("year" = 365.24, "month" = 30.4367, "week" = 7, "day" = 1)
 
  if (!time_scale %in% names(timeUnit)) stop("time_scale has to be 'year', 'month', 'week' or 'day'")
@@ -84,7 +83,7 @@ km_makeup <- function(km_fit_before, km_fit_after = NULL, time_scale, trt, trt_e
  # base plot
  par(mfrow = c(1, 1), bty = "n", tcl = -0.15, mgp = c(2.3, 0.5, 0))
  plot(0, 0,
- type = "n", xlab = paste0("Time in", time_scale), ylab = "Survival Probability",
+ type = "n", xlab = paste0("Time in ", time_scale), ylab = "Survival Probability",
  ylim = c(0, 1), xlim = t_range, yaxt = "n",
  main = paste0(
  "Kaplan-Meier Curves of Comparator ", ifelse(!is.null(km_fit_after), "(AgD) ", ""),
@@ -94,7 +93,7 @@ km_makeup <- function(km_fit_before, km_fit_after = NULL, time_scale, trt, trt_e
  )
  axis(2, las = 1)
 
- # add km lines from external trail
+ # add km lines from external trial
  lines(
  y = pd_be[[trt_ext]]$surv,
  x = (pd_be[[trt_ext]]$time / timeUnit[[time_scale]]), col = "#5450E4",
@@ -142,7 +141,7 @@ km_makeup <- function(km_fit_before, km_fit_after = NULL, time_scale, trt, trt_e
  legend = c(
  paste0("Comparator: ", trt_ext),
  paste0("Treatment: ", trt),
- paste0("Treatment: ", trt, "(with weights)")
+ paste0("Treatment: ", trt, " (with weights)")
  )[use_leg]
  )
 }
@@ -233,7 +232,7 @@ resid_plot <- function(coxobj, time_scale = "month", log_time = TRUE, endpoint_n
  cex = 0.9, col = "navyblue", yaxt = "n",
  ylab = "Unscaled Schoenfeld Residual", xlab = paste0(ifelse(log_time, "Log-", ""), "Time in ", time_scale),
  main = paste0(
- "Diagnosis Plot: Unscaled Schoenfeld Residual\nEndpoint: ", endpoint_name,
+ "Diagnostic Plot: Unscaled Schoenfeld Residual\nEndpoint: ", endpoint_name,
  ifelse(subtitle == "", "", "\n"), subtitle
  )
  )